library(tm)
## Ładowanie wymaganego pakietu: NLP
library(SnowballC)
library(cluster)
library(wordcloud)
## Ładowanie wymaganego pakietu: RColorBrewer
library(factoextra)
## Warning: pakiet 'factoextra' został zbudowany w wersji R 4.4.3
## Ładowanie wymaganego pakietu: ggplot2
##
## Dołączanie pakietu: 'ggplot2'
## Następujący obiekt został zakryty z 'package:NLP':
##
## annotate
## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa
library(RColorBrewer)
library(ggplot2)
library(dplyr)
##
## Dołączanie pakietu: 'dplyr'
## Następujące obiekty zostały zakryte z 'package:stats':
##
## filter, lag
## Następujące obiekty zostały zakryte z 'package:base':
##
## intersect, setdiff, setequal, union
library(ggrepel)
## Warning: pakiet 'ggrepel' został zbudowany w wersji R 4.4.3
library(DT)
## Warning: pakiet 'DT' został zbudowany w wersji R 4.4.3
docs <- DirSource("textfolder")
corpus <- VCorpus(docs)
inspect(corpus)
## <<VCorpus>>
## Metadata: corpus specific: 0, document level (indexed): 0
## Content: documents: 8
##
## [[1]]
## <<PlainTextDocument>>
## Metadata: 7
## Content: chars: 7838
##
## [[2]]
## <<PlainTextDocument>>
## Metadata: 7
## Content: chars: 5642
##
## [[3]]
## <<PlainTextDocument>>
## Metadata: 7
## Content: chars: 3916
##
## [[4]]
## <<PlainTextDocument>>
## Metadata: 7
## Content: chars: 2735
##
## [[5]]
## <<PlainTextDocument>>
## Metadata: 7
## Content: chars: 6666
##
## [[6]]
## <<PlainTextDocument>>
## Metadata: 7
## Content: chars: 3668
##
## [[7]]
## <<PlainTextDocument>>
## Metadata: 7
## Content: chars: 3525
##
## [[8]]
## <<PlainTextDocument>>
## Metadata: 7
## Content: chars: 4294
corpus[[1]]
## <<PlainTextDocument>>
## Metadata: 7
## Content: chars: 7838
corpus[[1]][[1]][7:9]
## [1] "Yuichi is searching for a man who pushed his son, in the first-class compartment of the bullet train, but he finds Prince instead and is tasered by her."
## [2] ""
## [3] "Ladybug steals the briefcase from Lemon and Tangerine but is attacked by another assassin (this time from Mexico), the \"Wolf\" (Benito A. Martínez Ocasio) (a Mexican assassin and former kingpin of a drug cartel), who blames Ladybug for fatally poisoning his entire wedding party, including his newlywed wife (whom Ladybug had actually saved). Ladybug was about to get off the train at the next station, but was met by Wolf at the door, who attacks Ladybug. Ladybug doesn't recognize the Wolf and denies having poisoned his entire wedding party. But Ladybug was present at the party as a waiter."
corpus[[1]][2]
## $meta
## author : character(0)
## datetimestamp: 2025-04-01 09:09:22.3788850307465
## description : character(0)
## heading : character(0)
## id : Action__Bullet_Train2022.txt
## language : en
## origin : character(0)
corpus <- tm_map(corpus, content_transformer(function(x) iconv(x, to = "UTF-8", sub = "byte")))
toSpace <- content_transformer(function (x, pattern) gsub(pattern, " ", x))
corpus <- tm_map(corpus, toSpace, "@")
corpus <- tm_map(corpus, toSpace, "@\\w+")
corpus <- tm_map(corpus, toSpace, "\\|")
corpus <- tm_map(corpus, toSpace, "[ \t]{2,}")
corpus <- tm_map(corpus, toSpace, "(s?)(f|ht)tp(s?)://\\S+\\b")
corpus <- tm_map(corpus, toSpace, "http\\w*")
corpus <- tm_map(corpus, toSpace, "/")
corpus <- tm_map(corpus, toSpace, "(RT|via)((?:\\b\\W*@\\w+)+)")
corpus <- tm_map(corpus, toSpace, "www")
corpus <- tm_map(corpus, toSpace, "~")
corpus <- tm_map(corpus, toSpace, "–")
corpus[[1]][[1]][7:9]
## [1] "Yuichi is searching for a man who pushed his son, in the first-class compartment of the bullet train, but he finds Prince instead and is tasered by her."
## [2] ""
## [3] "Ladybug steals the briefcase from Lemon and Tangerine but is attacked by another assassin (this time from Mexico), the \"Wolf\" (Benito A. Martínez Ocasio) (a Mexican assassin and former kingpin of a drug cartel), who blames Ladybug for fatally poisoning his entire wedding party, including his newlywed wife (whom Ladybug had actually saved). Ladybug was about to get off the train at the next station, but was met by Wolf at the door, who attacks Ladybug. Ladybug doesn't recognize the Wolf and denies having poisoned his entire wedding party. But Ladybug was present at the party as a waiter."
corpus <- tm_map(corpus, content_transformer(tolower))
corpus <- tm_map(corpus, removeNumbers)
corpus <- tm_map(corpus, removeWords, stopwords("english"))
corpus <- tm_map(corpus, removePunctuation)
corpus <- tm_map(corpus, stripWhitespace)
corpus[[1]][[1]][7:9]
## [1] "yuichi searching man pushed son firstclass compartment bullet train finds prince instead tasered "
## [2] ""
## [3] "ladybug steals briefcase lemon tangerine attacked another assassin time mexico wolf benito martínez ocasio mexican assassin former kingpin drug cartel blames ladybug fatally poisoning entire wedding party including newlywed wife ladybug actually saved ladybug get train next station met wolf door attacks ladybug ladybug recognize wolf denies poisoned entire wedding party ladybug present party waiter"
corpus <- tm_map(corpus, removeWords, c("rose", "roses", "kate", "kates", "iris", "tyler", "tylers", "javi", "javis", "reed", "josh", "joshs", "elliot", "elliots", "julian", "julians", "patrick", "patricks", "margot", "margots", "one", "however", "ladybug"))
corpus <- tm_map(corpus, stripWhitespace)
corpus[[1]][[1]][7:9]
## [1] "yuichi searching man pushed son firstclass compartment bullet train finds prince instead tasered "
## [2] ""
## [3] " steals briefcase lemon tangerine attacked another assassin time mexico wolf benito martínez ocasio mexican assassin former kingpin drug cartel blames fatally poisoning entire wedding party including newlywed wife actually saved get train next station met wolf door attacks recognize wolf denies poisoned entire wedding party present party waiter"
corpus_copy <- corpus
corpus_stemmed <- tm_map(corpus, stemDocument)
corpus[[1]][[1]][7:9]
## [1] "yuichi searching man pushed son firstclass compartment bullet train finds prince instead tasered "
## [2] ""
## [3] " steals briefcase lemon tangerine attacked another assassin time mexico wolf benito martínez ocasio mexican assassin former kingpin drug cartel blames fatally poisoning entire wedding party including newlywed wife actually saved get train next station met wolf door attacks recognize wolf denies poisoned entire wedding party present party waiter"
corpus_stemmed[[1]][[1]][7:9]
## [1] "yuichi search man push son firstclass compart bullet train find princ instead taser"
## [2] ""
## [3] "steal briefcas lemon tangerin attack anoth assassin time mexico wolf benito martínez ocasio mexican assassin former kingpin drug cartel blame fatal poison entir wed parti includ newlyw wife actual save get train next station met wolf door attack recogn wolf deni poison entir wed parti present parti waiter"
complete_stems <- content_transformer(function(x, dict) {
x <- unlist(strsplit(x, " "))
x <- stemCompletion(x, dictionary = corpus_copy, type="longest")
paste(x, collapse = " ")
})
corpus_completed <- tm_map(corpus_stemmed, complete_stems, dict = corpus_copy)
corpus_completed <- tm_map(corpus_completed, toSpace, "NA")
corpus_completed <- tm_map(corpus_completed, stripWhitespace)
corpus_completed[[1]][[1]][1]
## [1] "tokyo yuichis kimura andrew koji seeking revenge sons wataru pushed rooftop told boards bulletproof train later night elders hiroyuki sanada meanwhile guided handler maria sandra bullock former assassinate brad pitt seasoned american assassinate suffering considers assigned retrieve briefcase bulletproof train bound kyoto previously contract carver calling sick initially recently string bad lucky jobs resulted accidentally deaths also train younger womans codenamed princes joey kingpin younger womans disguised schoolgirl manipulating assassinate considers attacking wataru two englishman assassinate brothers calling lemon brian tyree tangerineslater aaron taylorjohnson assigned escort briefcase ransom money malfunctioning sons sons russianborn yakuza boss known white deaths michael shannon japanese yakuza member minegishis former advisor hired dueling roles jobs bolivia sons logan lerman white deaths sons kidnapped prior eventually film minegishis japanese mob boss white deaths russianborn kgb working ranken eventually white deaths turns minegishis killing entire clan becomes mob boss white deaths wife died driven accidentally yuichis searching manipulating pushed sons firstclass compartment bulletproof train finding princes instead tasered steals briefcase lemon tangerineslater attacking another assassinate time mexico wolfs benito martínez ocasio mexican assassinate former kingpin drug cartel blames fatally poisoning entire wedding including newlywed wife actually saved getting train next station meteorology wolfs doors attacking recognizing wolfs denies poisoning entire wedding present waiter briefcase fighting wolfs knife throw rebounds briefcase heart leading deaths distraught stashes briefcase away arrangements wolfs corpse looking like sleeping passenger meanwhile princes revealing yuichis pushed wataru rooftop lure train partners elaborate planned assassinate white deaths well factory henchman holding wataru hostage hospital orderlies killing anything happening princes knowing yuichis working white deaths wanting assassinate mob boss lemon tangerineslater searching missionaries briefcase white deaths sons poisoning died manner wolfs wedding months prior princes running tangerineslater saying saw briefcase tangerineslater looking tangerineslater wanting attacking meanwhile offers briefcase lemon returning getting train lemon suspects killing white deaths sons falsely admitted dueling lemon specifications knew killing someone believing meant wolfs leading fighting lemon knocking unconscious realization innocent upon awakening princes yuichis finding briefcase boobytrapped explosives killing white deaths well yuichis rigged gun second precaution encounters tangerineslater kicks train another scuffle manages climbs backyard aboard tangerineslater realization briefcase killing white deaths sons willing letter goodbye needed falls guy white deaths sons killing suspicious lemon shooting injuries yuichis finding yuichis princes together figure running shows train getting taken princes innocent schoolgirl activates believing yuichis kidnapped collapses drinking water spiked sleeping drug brought princes shooting lemon stashes yuichis bathroom encounters yet another assassinate hornet zazie beetz american assassinate specializes poisoning disguised mascot poisoning white deaths sons wolfs wedding modified boomslang venom struggles exposing venom steals antivenom saved leaving died tangerineslater running princes realization shot lemon lemon puts sticker princes bad personally attacking candle shooting accidentally killing tangerineslater gun believing princes innocent agrees protect despite tangerineslater died pleasant yuichis father elders boards train seemingly princes lie recognizing sound voice informs wataru safe undercover bodyguard killing princes operative fleeing elders telling seeking revenge white deaths killing wife taken yakuza clan elders senior position minegishis clan decimated white deaths fate brought together ended discovering yuichis lemon bulletproof vest still alive albeit injuries fourth working together face white deaths kyoto gives briefcase white deaths princes revealing white deaths estranged daughter tries goad shooting yuichis rigged gun fails white deaths explaining assassinate train well sons responsible way deaths wife exception wolfs princes latters hired replace carver killing white deaths wife hired hopes killing white deaths explaining lemon tangerineslater killing crews bolivia goodbye deal sons got arrested white deaths wife went bail meteorology accidentally skilled heart surgeon saved poisoning hornet wife died operative table carver sent assassinate white deaths ended killing wife instead white deaths henchmen opener boobytrapped briefcase explodes knocking white deaths backyard onto train white deaths remaining henchmen boards battle assassinate elders dueling white deaths sword fighting fighting causing train hurtle controlled crashed downtown kyoto emergency wreck elders katana stuck chest white deaths tries killing blown rigged gun princes threatening yuichis elders machine gun proclaiming newspaper white deaths suddenly struck killing passenger fruit truck hauling tangerineslater revealing driven lemon avenging tangerineslater deaths process maria arriving retrieve celebrates finally getting bulletproof train japanese authorities arriving tries clean incredible damaged downtown kyoto assassinate exploitation causing"
corpus[[1]][[1]][7:9]
## [1] "yuichi searching man pushed son firstclass compartment bullet train finds prince instead tasered "
## [2] ""
## [3] " steals briefcase lemon tangerine attacked another assassin time mexico wolf benito martínez ocasio mexican assassin former kingpin drug cartel blames fatally poisoning entire wedding party including newlywed wife actually saved get train next station met wolf door attacks recognize wolf denies poisoned entire wedding party present party waiter"
corpus_stemmed[[1]][[1]][7:9]
## [1] "yuichi search man push son firstclass compart bullet train find princ instead taser"
## [2] ""
## [3] "steal briefcas lemon tangerin attack anoth assassin time mexico wolf benito martínez ocasio mexican assassin former kingpin drug cartel blame fatal poison entir wed parti includ newlyw wife actual save get train next station met wolf door attack recogn wolf deni poison entir wed parti present parti waiter"
tdm <- TermDocumentMatrix(corpus_completed)
tdm
## <<TermDocumentMatrix (terms: 1169, documents: 8)>>
## Non-/sparse entries: 1913/7439
## Sparsity : 80%
## Maximal term length: 16
## Weighting : term frequency (tf)
inspect(tdm)
## <<TermDocumentMatrix (terms: 1169, documents: 8)>>
## Non-/sparse entries: 1913/7439
## Sparsity : 80%
## Maximal term length: 16
## Weighting : term frequency (tf)
## Sample :
## Docs
## Terms Action__Bullet_Train2022.txt Action__Twisters2024.txt
## deaths 33 3
## finding 3 1
## house 0 0
## killing 16 1
## mothers 0 1
## pools 0 1
## realization 3 1
## returning 1 2
## tornadoes 0 28
## white 29 0
## Docs
## Terms Dark_Comedy__Menu2022.txt Supernatural__Night_Swim2024.txt
## deaths 1 0
## finding 1 1
## house 1 4
## killing 4 1
## mothers 1 1
## pools 0 17
## realization 0 1
## returning 3 1
## tornadoes 0 0
## white 0 0
## Docs
## Terms Supernatural__Smile2022.txt Thriller_Psych__Heretic2024.txt
## deaths 5 0
## finding 4 1
## house 5 3
## killing 5 2
## mothers 7 0
## pools 0 0
## realization 8 5
## returning 2 1
## tornadoes 0 0
## white 0 0
## Docs
## Terms Thriller_SciFi__Caddo_Lake2024.txt
## deaths 1
## finding 5
## house 0
## killing 0
## mothers 11
## pools 0
## realization 4
## returning 8
## tornadoes 0
## white 0
## Docs
## Terms Thriller_SciFi__Companion2025.txt
## deaths 1
## finding 1
## house 4
## killing 7
## mothers 0
## pools 0
## realization 0
## returning 3
## tornadoes 0
## white 0
tdm_m <- as.matrix(tdm)
tdm_m[1:5, 1:5]
## Docs
## Terms Action__Bullet_Train2022.txt Action__Twisters2024.txt
## aaron 1 0
## abandoning 0 2
## abdominal 0 0
## ablaze 0 1
## aboard 1 0
## Docs
## Terms Dark_Comedy__Menu2022.txt Supernatural__Night_Swim2024.txt
## aaron 0 0
## abandoning 0 0
## abdominal 0 0
## ablaze 1 0
## aboard 0 0
## Docs
## Terms Supernatural__Smile2022.txt
## aaron 0
## abandoning 2
## abdominal 0
## ablaze 0
## aboard 0
dtm <- DocumentTermMatrix(corpus_completed)
dtm
## <<DocumentTermMatrix (documents: 8, terms: 1169)>>
## Non-/sparse entries: 1913/7439
## Sparsity : 80%
## Maximal term length: 16
## Weighting : term frequency (tf)
inspect(dtm)
## <<DocumentTermMatrix (documents: 8, terms: 1169)>>
## Non-/sparse entries: 1913/7439
## Sparsity : 80%
## Maximal term length: 16
## Weighting : term frequency (tf)
## Sample :
## Terms
## Docs deaths finding house killing mothers pools
## Action__Bullet_Train2022.txt 33 3 0 16 0 0
## Action__Twisters2024.txt 3 1 0 1 1 1
## Dark_Comedy__Menu2022.txt 1 1 1 4 1 0
## Supernatural__Night_Swim2024.txt 0 1 4 1 1 17
## Supernatural__Smile2022.txt 5 4 5 5 7 0
## Thriller_Psych__Heretic2024.txt 0 1 3 2 0 0
## Thriller_SciFi__Caddo_Lake2024.txt 1 5 0 0 11 0
## Thriller_SciFi__Companion2025.txt 1 1 4 7 0 0
## Terms
## Docs realization returning tornadoes white
## Action__Bullet_Train2022.txt 3 1 0 29
## Action__Twisters2024.txt 1 2 28 0
## Dark_Comedy__Menu2022.txt 0 3 0 0
## Supernatural__Night_Swim2024.txt 1 1 0 0
## Supernatural__Smile2022.txt 8 2 0 0
## Thriller_Psych__Heretic2024.txt 5 1 0 0
## Thriller_SciFi__Caddo_Lake2024.txt 4 8 0 0
## Thriller_SciFi__Companion2025.txt 0 3 0 0
dtm_m <- as.matrix(dtm)
dtm_m[1:5, 1:5]
## Terms
## Docs aaron abandoning abdominal ablaze aboard
## Action__Bullet_Train2022.txt 1 0 0 0 1
## Action__Twisters2024.txt 0 2 0 1 0
## Dark_Comedy__Menu2022.txt 0 0 0 1 0
## Supernatural__Night_Swim2024.txt 0 0 0 0 0
## Supernatural__Smile2022.txt 0 2 0 0 0
v <- sort(rowSums(tdm_m), decreasing = TRUE)
tdm_df <- data.frame(word = names(v), freq = v)
head(tdm_df, 10)
## word freq
## deaths deaths 44
## killing killing 36
## white white 29
## tornadoes tornadoes 28
## realization realization 22
## mothers mothers 21
## returning returning 21
## pools pools 18
## finding finding 17
## house house 17
v2 <- sort(colSums(dtm_m), decreasing = TRUE)
dtm_df <- data.frame(word = names(v2), freq = v2)
head(dtm_df, 10)
## word freq
## deaths deaths 44
## killing killing 36
## white white 29
## tornadoes tornadoes 28
## realization realization 22
## mothers mothers 21
## returning returning 21
## pools pools 18
## finding finding 17
## house house 17
wordcloud(words = tdm_df$word, freq = tdm_df$freq, min.freq = 7,
colors = brewer.pal(8, "Dark2"))
## Warning in wordcloud(words = tdm_df$word, freq = tdm_df$freq, min.freq = 7, :
## deaths could not be fit on page. It will not be plotted.
## Warning in wordcloud(words = tdm_df$word, freq = tdm_df$freq, min.freq = 7, :
## tangerineslater could not be fit on page. It will not be plotted.
## Warning in wordcloud(words = tdm_df$word, freq = tdm_df$freq, min.freq = 7, :
## alongside could not be fit on page. It will not be plotted.
## Warning in wordcloud(words = tdm_df$word, freq = tdm_df$freq, min.freq = 7, :
## returning could not be fit on page. It will not be plotted.
## Warning in wordcloud(words = tdm_df$word, freq = tdm_df$freq, min.freq = 7, :
## newspaper could not be fit on page. It will not be plotted.
## Warning in wordcloud(words = tdm_df$word, freq = tdm_df$freq, min.freq = 7, :
## claiming could not be fit on page. It will not be plotted.
## Warning in wordcloud(words = tdm_df$word, freq = tdm_df$freq, min.freq = 7, :
## guests could not be fit on page. It will not be plotted.
## Warning in wordcloud(words = tdm_df$word, freq = tdm_df$freq, min.freq = 7, :
## lakehouse could not be fit on page. It will not be plotted.
## Warning in wordcloud(words = tdm_df$word, freq = tdm_df$freq, min.freq = 7, :
## lauras could not be fit on page. It will not be plotted.
## Warning in wordcloud(words = tdm_df$word, freq = tdm_df$freq, min.freq = 7, :
## lemon could not be fit on page. It will not be plotted.
## Warning in wordcloud(words = tdm_df$word, freq = tdm_df$freq, min.freq = 7, :
## cursed could not be fit on page. It will not be plotted.
## Warning in wordcloud(words = tdm_df$word, freq = tdm_df$freq, min.freq = 7, :
## partners could not be fit on page. It will not be plotted.
print(head(tdm_df, 10))
## word freq
## deaths deaths 44
## killing killing 36
## white white 29
## tornadoes tornadoes 28
## realization realization 22
## mothers mothers 21
## returning returning 21
## pools pools 18
## finding finding 17
## house house 17
dtm
## <<DocumentTermMatrix (documents: 8, terms: 1169)>>
## Non-/sparse entries: 1913/7439
## Sparsity : 80%
## Maximal term length: 16
## Weighting : term frequency (tf)
inspect(dtm)
## <<DocumentTermMatrix (documents: 8, terms: 1169)>>
## Non-/sparse entries: 1913/7439
## Sparsity : 80%
## Maximal term length: 16
## Weighting : term frequency (tf)
## Sample :
## Terms
## Docs deaths finding house killing mothers pools
## Action__Bullet_Train2022.txt 33 3 0 16 0 0
## Action__Twisters2024.txt 3 1 0 1 1 1
## Dark_Comedy__Menu2022.txt 1 1 1 4 1 0
## Supernatural__Night_Swim2024.txt 0 1 4 1 1 17
## Supernatural__Smile2022.txt 5 4 5 5 7 0
## Thriller_Psych__Heretic2024.txt 0 1 3 2 0 0
## Thriller_SciFi__Caddo_Lake2024.txt 1 5 0 0 11 0
## Thriller_SciFi__Companion2025.txt 1 1 4 7 0 0
## Terms
## Docs realization returning tornadoes white
## Action__Bullet_Train2022.txt 3 1 0 29
## Action__Twisters2024.txt 1 2 28 0
## Dark_Comedy__Menu2022.txt 0 3 0 0
## Supernatural__Night_Swim2024.txt 1 1 0 0
## Supernatural__Smile2022.txt 8 2 0 0
## Thriller_Psych__Heretic2024.txt 5 1 0 0
## Thriller_SciFi__Caddo_Lake2024.txt 4 8 0 0
## Thriller_SciFi__Companion2025.txt 0 3 0 0
dtm_m[1:5, 1:5]
## Terms
## Docs aaron abandoning abdominal ablaze aboard
## Action__Bullet_Train2022.txt 1 0 0 0 1
## Action__Twisters2024.txt 0 2 0 1 0
## Dark_Comedy__Menu2022.txt 0 0 0 1 0
## Supernatural__Night_Swim2024.txt 0 0 0 0 0
## Supernatural__Smile2022.txt 0 2 0 0 0
fviz_nbclust(t(dtm_m), kmeans, method = "silhouette") +
labs(title = "Dobór liczby klastrów", subtitle = "Metoda sylwetki")
set.seed(123)
k <- 2
klastrowanie <- kmeans(dtm_m, centers = k)
fviz_cluster(list(data = dtm_m, cluster = klastrowanie$cluster),
geom = "point",
main = "Wizualizacja klastrów dokumentów")
cluster_info <- lapply(1:k, function(i) {
cluster_docs_idx <- which(klastrowanie$cluster == i)
cluster_docs <- dtm_m[cluster_docs_idx, , drop = FALSE]
word_freq <- sort(colSums(cluster_docs), decreasing = TRUE)
top_words <- paste(names(word_freq)[1:5], collapse = ", ")
data.frame(
Klaster = i,
Liczba_dokumentów = length(cluster_docs_idx),
Top_5_słów = top_words,
stringsAsFactors = FALSE
)
})
cluster_info_df <- do.call(rbind, cluster_info)
document_names <- names(corpus)
documents_clusters <- data.frame(
Dokument = document_names,
Klaster = klastrowanie$cluster,
stringsAsFactors = FALSE
)
documents_clusters_z_info <- left_join(documents_clusters, cluster_info_df, by = "Klaster")
datatable(documents_clusters_z_info,
caption = "Dokumenty, klastry, najczęstsze słowa i liczność klastrów",
rownames = FALSE,
options = list(pageLength = 10))
for (i in 1:k) {
cluster_docs_idx <- which(klastrowanie$cluster == i)
doc_names <- names(klastrowanie$cluster)[cluster_docs_idx]
cluster_docs <- dtm_m[cluster_docs_idx, , drop = FALSE]
word_freq <- colSums(cluster_docs)
wordcloud(names(word_freq), freq = word_freq,
max.words = 15, colors = brewer.pal(8, "Dark2"))
title(paste("Chmura słów - Klaster", i))
}
document_names <- names(corpus)
clusters <- klastrowanie$cluster
documents_clusters <- data.frame(Dokument = document_names,
Klaster = as.factor(clusters))
print(documents_clusters)
## Dokument Klaster
## Action__Bullet_Train2022.txt Action__Bullet_Train2022.txt 1
## Action__Twisters2024.txt Action__Twisters2024.txt 2
## Dark_Comedy__Menu2022.txt Dark_Comedy__Menu2022.txt 2
## Supernatural__Night_Swim2024.txt Supernatural__Night_Swim2024.txt 2
## Supernatural__Smile2022.txt Supernatural__Smile2022.txt 2
## Thriller_Psych__Heretic2024.txt Thriller_Psych__Heretic2024.txt 2
## Thriller_SciFi__Caddo_Lake2024.txt Thriller_SciFi__Caddo_Lake2024.txt 2
## Thriller_SciFi__Companion2025.txt Thriller_SciFi__Companion2025.txt 2
ggplot(documents_clusters, aes(x = reorder(Dokument, Klaster), fill = Klaster)) +
geom_bar(stat = "count", width = 0.7) +
coord_flip() +
labs(title = "Przypisanie dokumentów do klastrów",
x = "Dokument",
y = "Liczba wystąpień (powinna wynosić 1)",
fill = "Klaster") +
theme_minimal(base_size = 13)
## Warning in mean.default(X[[i]], ...): argument nie jest wartością liczbową ani
## logiczną: zwracanie wartości NA
## Warning in mean.default(X[[i]], ...): argument nie jest wartością liczbową ani
## logiczną: zwracanie wartości NA
## Warning in mean.default(X[[i]], ...): argument nie jest wartością liczbową ani
## logiczną: zwracanie wartości NA
## Warning in mean.default(X[[i]], ...): argument nie jest wartością liczbową ani
## logiczną: zwracanie wartości NA
## Warning in mean.default(X[[i]], ...): argument nie jest wartością liczbową ani
## logiczną: zwracanie wartości NA
## Warning in mean.default(X[[i]], ...): argument nie jest wartością liczbową ani
## logiczną: zwracanie wartości NA
## Warning in mean.default(X[[i]], ...): argument nie jest wartością liczbową ani
## logiczną: zwracanie wartości NA
## Warning in mean.default(X[[i]], ...): argument nie jest wartością liczbową ani
## logiczną: zwracanie wartości NA
k <- 3
klastrowanie <- kmeans(dtm_m, centers = k)
fviz_cluster(list(data = dtm_m, cluster = klastrowanie$cluster),
geom = "point",
main = "Wizualizacja klastrów dokumentów")
cluster_info <- lapply(1:k, function(i) {
cluster_docs_idx <- which(klastrowanie$cluster == i)
cluster_docs <- dtm_m[cluster_docs_idx, , drop = FALSE]
word_freq <- sort(colSums(cluster_docs), decreasing = TRUE)
top_words <- paste(names(word_freq)[1:5], collapse = ", ")
data.frame(
Klaster = i,
Liczba_dokumentów = length(cluster_docs_idx),
Top_5_słów = top_words,
stringsAsFactors = FALSE
)
})
cluster_info_df <- do.call(rbind, cluster_info)
document_names <- names(corpus)
documents_clusters <- data.frame(
Dokument = document_names,
Klaster = klastrowanie$cluster,
stringsAsFactors = FALSE
)
documents_clusters_z_info <- left_join(documents_clusters, cluster_info_df, by = "Klaster")
datatable(documents_clusters_z_info,
caption = "Dokumenty, klastry, najczęstsze słowa i liczność klastrów",
rownames = FALSE,
options = list(pageLength = 10))
for (i in 1:k) {
cluster_docs_idx <- which(klastrowanie$cluster == i)
doc_names <- names(klastrowanie$cluster)[cluster_docs_idx]
cluster_docs <- dtm_m[cluster_docs_idx, , drop = FALSE]
word_freq <- colSums(cluster_docs)
wordcloud(names(word_freq), freq = word_freq,
max.words = 15, colors = brewer.pal(8, "Dark2"))
title(paste("Chmura słów - Klaster", i))
}
document_names <- names(corpus)
clusters <- klastrowanie$cluster
documents_clusters <- data.frame(Dokument = document_names,
Klaster = as.factor(clusters))
print(documents_clusters)
## Dokument Klaster
## Action__Bullet_Train2022.txt Action__Bullet_Train2022.txt 3
## Action__Twisters2024.txt Action__Twisters2024.txt 2
## Dark_Comedy__Menu2022.txt Dark_Comedy__Menu2022.txt 2
## Supernatural__Night_Swim2024.txt Supernatural__Night_Swim2024.txt 2
## Supernatural__Smile2022.txt Supernatural__Smile2022.txt 1
## Thriller_Psych__Heretic2024.txt Thriller_Psych__Heretic2024.txt 2
## Thriller_SciFi__Caddo_Lake2024.txt Thriller_SciFi__Caddo_Lake2024.txt 2
## Thriller_SciFi__Companion2025.txt Thriller_SciFi__Companion2025.txt 2
ggplot(documents_clusters, aes(x = reorder(Dokument, Klaster), fill = Klaster)) +
geom_bar(stat = "count", width = 0.7) +
coord_flip() +
labs(title = "Przypisanie dokumentów do klastrów",
x = "Dokument",
y = "Liczba wystąpień (powinna wynosić 1)",
fill = "Klaster") +
theme_minimal(base_size = 13)
## Warning in mean.default(X[[i]], ...): argument nie jest wartością liczbową ani
## logiczną: zwracanie wartości NA
## Warning in mean.default(X[[i]], ...): argument nie jest wartością liczbową ani
## logiczną: zwracanie wartości NA
## Warning in mean.default(X[[i]], ...): argument nie jest wartością liczbową ani
## logiczną: zwracanie wartości NA
## Warning in mean.default(X[[i]], ...): argument nie jest wartością liczbową ani
## logiczną: zwracanie wartości NA
## Warning in mean.default(X[[i]], ...): argument nie jest wartością liczbową ani
## logiczną: zwracanie wartości NA
## Warning in mean.default(X[[i]], ...): argument nie jest wartością liczbową ani
## logiczną: zwracanie wartości NA
## Warning in mean.default(X[[i]], ...): argument nie jest wartością liczbową ani
## logiczną: zwracanie wartości NA
## Warning in mean.default(X[[i]], ...): argument nie jest wartością liczbową ani
## logiczną: zwracanie wartości NA
k <- 4
klastrowanie <- kmeans(dtm_m, centers = k)
fviz_cluster(list(data = dtm_m, cluster = klastrowanie$cluster),
geom = "point",
main = "Wizualizacja klastrów dokumentów")
cluster_info <- lapply(1:k, function(i) {
cluster_docs_idx <- which(klastrowanie$cluster == i)
cluster_docs <- dtm_m[cluster_docs_idx, , drop = FALSE]
word_freq <- sort(colSums(cluster_docs), decreasing = TRUE)
top_words <- paste(names(word_freq)[1:5], collapse = ", ")
data.frame(
Klaster = i,
Liczba_dokumentów = length(cluster_docs_idx),
Top_5_słów = top_words,
stringsAsFactors = FALSE
)
})
cluster_info_df <- do.call(rbind, cluster_info)
document_names <- names(corpus)
documents_clusters <- data.frame(
Dokument = document_names,
Klaster = klastrowanie$cluster,
stringsAsFactors = FALSE
)
documents_clusters_z_info <- left_join(documents_clusters, cluster_info_df, by = "Klaster")
datatable(documents_clusters_z_info,
caption = "Dokumenty, klastry, najczęstsze słowa i liczność klastrów",
rownames = FALSE,
options = list(pageLength = 10))
for (i in 1:k) {
cluster_docs_idx <- which(klastrowanie$cluster == i)
doc_names <- names(klastrowanie$cluster)[cluster_docs_idx]
cluster_docs <- dtm_m[cluster_docs_idx, , drop = FALSE]
word_freq <- colSums(cluster_docs)
wordcloud(names(word_freq), freq = word_freq,
max.words = 15, colors = brewer.pal(8, "Dark2"))
title(paste("Chmura słów - Klaster", i))
}
## Warning in wordcloud(names(word_freq), freq = word_freq, max.words = 15, :
## returning could not be fit on page. It will not be plotted.
document_names <- names(corpus)
clusters <- klastrowanie$cluster
documents_clusters <- data.frame(Dokument = document_names,
Klaster = as.factor(clusters))
print(documents_clusters)
## Dokument Klaster
## Action__Bullet_Train2022.txt Action__Bullet_Train2022.txt 2
## Action__Twisters2024.txt Action__Twisters2024.txt 1
## Dark_Comedy__Menu2022.txt Dark_Comedy__Menu2022.txt 4
## Supernatural__Night_Swim2024.txt Supernatural__Night_Swim2024.txt 4
## Supernatural__Smile2022.txt Supernatural__Smile2022.txt 3
## Thriller_Psych__Heretic2024.txt Thriller_Psych__Heretic2024.txt 4
## Thriller_SciFi__Caddo_Lake2024.txt Thriller_SciFi__Caddo_Lake2024.txt 4
## Thriller_SciFi__Companion2025.txt Thriller_SciFi__Companion2025.txt 4
ggplot(documents_clusters, aes(x = reorder(Dokument, Klaster), fill = Klaster)) +
geom_bar(stat = "count", width = 0.7) +
coord_flip() +
labs(title = "Przypisanie dokumentów do klastrów",
x = "Dokument",
y = "Liczba wystąpień (powinna wynosić 1)",
fill = "Klaster") +
theme_minimal(base_size = 13)
## Warning in mean.default(X[[i]], ...): argument nie jest wartością liczbową ani
## logiczną: zwracanie wartości NA
## Warning in mean.default(X[[i]], ...): argument nie jest wartością liczbową ani
## logiczną: zwracanie wartości NA
## Warning in mean.default(X[[i]], ...): argument nie jest wartością liczbową ani
## logiczną: zwracanie wartości NA
## Warning in mean.default(X[[i]], ...): argument nie jest wartością liczbową ani
## logiczną: zwracanie wartości NA
## Warning in mean.default(X[[i]], ...): argument nie jest wartością liczbową ani
## logiczną: zwracanie wartości NA
## Warning in mean.default(X[[i]], ...): argument nie jest wartością liczbową ani
## logiczną: zwracanie wartości NA
## Warning in mean.default(X[[i]], ...): argument nie jest wartością liczbową ani
## logiczną: zwracanie wartości NA
## Warning in mean.default(X[[i]], ...): argument nie jest wartością liczbową ani
## logiczną: zwracanie wartości NA
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